peak calling and annotation in scrap for small noncoding rna: target rna interaction studies

体内 RNA：使用 SCRAP 生物信息学管道的 RNA 相互作用

Small noncoding RNAs are highly studied for their post-transcriptional roles in coordinating expression from suites of genes in diverse processes such as differentiation and development, signal processing, and disease1,2,3. The ability to accurately determine the target transcripts of gene-regulatory small noncoding RNAs (sncRNAs), including microRNAs (miRNAs), is of importance to studies of RNA biology at both basic and translational levels. Bioinformatic algorithms that exploit anticipated complementarity between the miRNA seed sequence and its potential targets have been frequently used for the prediction of miRNA:target RNA interactions. While these bioinformatic algorithms have been successful, they also can harbor both false positive and false negative results, as has been reviewed elsewhere4,5,6. Recently, several biochemical approaches have been designed and implemented that allow unambiguous and semiquantitative determination of in vivo sncRNA:target RNA interactions by in vivo crosslinking and ensuing incorporation of a ligation step to physically attach the sncRNA to its target to form a single chimeric RNA4,5,7,8,9,10. Subsequent preparation of sequencing libraries from the chimeric RNAs allows assessment of the sncRNA:target RNA interactions by computational processing of the sequencing data. This video provides a tutorial for installing and using a computational pipeline termed small chimeric RNA analysis pipeline (SCRAP), which is designed to allow robust and reproducible analysis of sncRNA:target RNA interactions from chimeric RNA sequencing libraries6.
A goal of this tutorial is to assist investigators in avoiding excessive reliance on purely predictive bioinformatic algorithms by lowering barriers to the analysis of data generated through biochemical approaches providing chimeric molecular readouts of sncRNA:target RNA interactions. This tutorial provides practical steps and tips to guide entry-level computational scientists through the use of a pipeline, SCRAP, developed for analyzing chimeric RNA sequencing data, which can be generated by several existing biochemical protocols, including crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous Argonaute-bound RNAs- crosslinking and immunoprecipitation (CLEAR-CLIP)7,9.
The use of SCRAP offers several advantages for the analysis of chimeric RNA sequencing data, compared to other computational pipelines6. One salient advantage is its extensive annotation and the incorporation of call-outs to well-supported and routinely updated bioinformatic scripts within the pipeline, in comparison to alternative pipelines that often rely on custom and/or unsupported scripts for steps in the pipeline. This feature lends stability to SCRAP, making it more worthwhile for researchers to familiarize themselves with the pipeline and to incorporate its use into their workflow. SCRAP has also been demonstrated to outperform alternative pipelines in calling peaks of sncRNA:target RNA interactions and to have cross-platform functionality, as detailed in a prior publication6.
By the end of this tutorial, users will be able to (i) know platform requirements for SCRAP and install SCRAP pipelines, (ii) install reference genomes and set up command line parameters for SCRAP, and (iii) understand peak calling criteria and perform peak calling and peak annotation.
This video will describe in practical detail how researchers studying RNA biology may install and optimally use the computational pipeline, SCRAP, to analyze sncRNA interactions with target RNAs, such as messenger RNAs, in chimeric RNA-sequencing data obtained through one of the discussed biochemical approaches to sequencing library preparation.
SCRAP is a command line utility. Generally, following the guide below, the user will need to (i) download and install SCRAP (https://github.com/Meffert-Lab/SCRAP), (ii) Install reference genomes and run SCRAP, and (iii) perform peak calling and annotation.
Further details of the computational steps in this procedure can be found at&#160;https://github.com/Meffert-Lab/SCRAP. This article will provide the setup and background information to allow investigators with entry-level computational skills to install, optimize, and use SCRAP on chimeric RNA sequencing library datasets.

作者聚焦：用于分析高通量测序数据中嵌合非编码 RNA-靶 RNA 相互作用的计算管道 

嵌合小非编码RNA的计算分析教程：靶标RNA测序文库

This protocol covers the installation and practical steps for the use of a computational pipeline designed to analyze high throughput sequencing data from chimeric noncoding RNA-target RNA-interactions. The molecular strategy to form chimeric RNAs is a relatively recent development that has advantages when compared to prior biochromatic prediction tools in unambiguously understanding the genome-wide noncoding RNA-target RNA-interaction landscape. Sequencing data generated from the high throughput sequencing of the chimeric RNAs can present an analysis challenge for new adopters of this strategy.We have established an open source cross-platform computational pipeline that is highly annotated to allow entry-level users to analyze data from their chimeric noncoding RNA-target RNA sequencing experiments. In future experiments, the Mefford lab plans to use computational analysis by scrap on dataset generated from our ongoing investigations into post transcriptional regulation in the mammalian nervous system, as well as to maintain an update scrap on our GitHub.

Watch this Scientific Journal Video about Peak Calling and Annotation in SCRAP for Small Noncoding RNA: Target RNA Interaction Studies at JoVE.com

Peak Calling and Annotation in SCRAP for Small Noncoding RNA: Target RNA Interaction Studies  

SCRAP 中小非编码 RNA 的峰检出和注释：靶标 RNA 相互作用研究

运行 SCRAP 后，使用指示的脚本命令执行峰值调用。列出包含示例文件夹和适配器文件的目录的完整路径。然后，定义峰检出的标准，包括最小测序读长数，以及识别峰所需的不同测序文库数。指示峰是否必须由同一家族的 sncRNA 贡献，这与共享种子序列并可与重叠基因靶标结合的 miRNA 相关。然后，指示参考目录的完整路径、MIR 碱基缩写和参考基因组缩写。高峰调用后，运行高峰注解脚本。提供所得峰的完整路径。床从峰调用到参考目录和所需的物种进行注释。使用 samtools merge 合并所有需要的 bam 文件，以便于组合可视化。然后，使用 samtools 排序对合并进行逐行排序。BAM 文件。为排序后编制索引。BAM文件，生成基因组可视化工具所需的二进制SAMToS 格式索引文件。最后，在综合基因组学查看器中，打开排序。BAM 和索引。按文件。将SCRAP的修改版本应用于先前发表的测序数据集，揭示了miRNA与内含子区域的相互作用减少。在通过与 SCRAP 的峰值调用分离高置信度相互作用后观察到减少。

peak-calling-annotation-scrap-for-small-noncoding-rna-target-rna

Research

JoVE Journal

Biology

Peak Calling and Annotation in SCRAP for Small Noncoding RNA: Target RNA Interaction Studies

61 次观看. 运行 SCRAP 后，使用指示的脚本命令执行峰值调用。列出包含示例文件夹和适配器文件的目录的完整路径。然后，定义峰检出的标准，包括最小测序读长数，以及识别峰所需的不同测序文库数。指示峰是否必须由同一家族的 sncRNA 贡献，这与共享种子序列并可与重叠基因靶标结合的 miRNA 相关。然后，指示参考目录的完整路径、MIR 碱基缩写和参考基因组缩写。高峰调用后?...

视频: Peak Calling and Annotation in SCRAP for Small Noncoding RNA: Target RNA Interaction Studies  

Computational Analysis Tutorial for Chimeric Small Noncoding RNA: Target RNA Sequencing Libraries

An understanding of the in vivo gene regulatory interactions of small noncoding RNAs (sncRNAs), such as microRNAs (miRNAs), with their target RNAs has been advanced in recent years by biochemical approaches which use cross-linking followed by ligation to capture sncRNA:target RNA interactions through the formation of chimeric RNAs and subsequent sequencing libraries. While datasets from chimeric RNA sequencing provide genome-wide and substantially less ambiguous input than miRNA prediction software, distilling this data into meaningful and actionable information requires additional analyses and may dissuade investigators lacking a computational background. This report provides a tutorial to support entry-level computational biologists in installing and applying a recent open-source software tool: Small Chimeric RNA Analysis Pipeline (SCRAP). Platform requirements, updates, and an explanation of pipeline steps and manipulation of key user-input variables is provided. Reducing a barrier for biologists to gain insights from chimeric RNA sequencing approaches has the potential to springboard discovery-based investigations of regulatory sncRNA:target RNA interactions in multiple biological contexts.

Here, we present a protocol demonstrating the installation and use of a bioinformatics pipeline to analyze chimeric RNA sequencing data used in the study of in vivo RNA:RNA interactions.

An understanding of the in vivo gene regulatory interactions of small noncoding RNAs (sncRNAs), such as microRNAs (miRNAs), with their target RNAs has ...

科研

生物学

Installing and Running SCRAP for Small Noncoding RNA-mRNA Interaction Analysis

Before initiating SCRAP installation on the Mac OSX platform, execute the command which git in the terminal to confirm the existence of Git. To verify Miniconda installation, type which conda in the terminal. To install Miniconda, refer to the Platform Setup markdown file on the Meffert Lab GitHub Repository for instructions to install on Windows, Mac OS, and Ubuntu For Mac systems, using the Home Brew Package Manager, use the command brew install miniconda to install Miniconda.To install Conda, run conda init, specifying the shell in use, then close and reopen the shell. A successful installation displays the base environment activated within the terminal session. Next, run the indicated git clone to obtain the latest copy of the SCRAP source code.Install Mamba, an improved package solver for Conda. Using the following command, install all the dependencies for SCRAP from SCRAP_environment. yml to its own Conda environment.Next, use the indicated bash script, specific to the organism whose SNC RNA mRNA interactions are analyzed for SCRAP. Provide the directory of the SCRAP source folder and perform the installation steps using the files within the FASTA and annotation folders. Ensure the directory path is complete and ends with a slash.Refer to the tables in ReadMe. md for the correct miRBase species abbreviations. For updated reference genome, use the UCSC Genome Browser or the NCBI Genome Data Hub.Inspect the corresponding species.annotation. bed files in the annotation directory in the SCRAP source folder. If there's a need to analyze a different species, provide an indicated file.After installing dependencies and SCRAP, use the indicated script to initiate SCRAP analysis with the specified command structure. List the full path to the sample directories without any shorthand and format the sample directories with the folder name matching the sample name. Emphasize that the path listed leads to the directory containing all the sample folders and not to an individual sample folder or file.Next, list the entire path to the adapter file. Verify that the sample names in the adapter file match the previously mentioned folder names and file names. Specify the sample type, either paired end or single end, and state the choice of RNA filters for pre-mRNAs, tRNAs, and optionally RNA cleaning.List the entire path to the reference directory. miRBase abbreviation and the reference genome abbreviation.

安装并运行 SCRAP 以进行小的非编码 RNA-mRNA 相互作用分析

After running SCRAP, use the indicated script command to perform peak calling. List the full paths to the directory containing the sample folders and the adapter file. Then, define the criteria for peak calling, including the minimum number of sequencing reads, and the number of distinct sequencing libraries needed for a peak to be recognized.Indicate whether peaks must be contributed to by sncRNAs of the same family, which is relevant for miRNAs that share seed sequences and can bind to overlapping gene targets. Then, indicate the full path to the reference directory, MIR base abbreviation, and the reference genome abbreviation. After peak calling, run the peak annotation script.Provide the full path to the resulting peaks. bed from the peak calling to the reference directory and the desired species for annotation. Use samtools merge to merge all the desired bam files to facilitate combined visualization.Then, use the samtools sort for line-by-line sorting of merged. bam file. Index the sorted.bam file using the samtools index, generating a binary samtools format index file required for genomic visualization tools. Finally, in the integrative genomics viewer, open the sorted. bam and indexed.by file. The application of a modified version of SCRAP to previously published sequencing data sets revealed a decrease in miRNA interactions with Intron regions. The reduction was observed after isolating high confidence interactions through peak calling with SCRAP.